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Asymptotic Theory of Outlier Detection Algorithms for Linear Time Series Regression Models
Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avis › Tidsskriftartikel › Forskning › peer review
Dokumenter
- Asymptotic_Theory_of_Outlier_Detection_Algorithms_for_Linear_Time_Series_Regression_Models_accepted_manuscript_2016
Accepteret manuskript, 499 KB, PDF-dokument
DOI
- https://doi.org/10.1111/sjos.12174
Forlagets udgivne version
- Søren Johansen
- Bent Nielsen, University of Oxford, Storbritannien
Outlier detection algorithms are intimately connected with robust statistics that down-weight some observations to zero. We define a number of outlier detection algorithms related to the Huber-skip and least trimmed squares estimators, including the one-step Huber-skip estimator and the forward search. Next, we review a recently developed asymptotic theory of these. Finally, we analyse the gauge, the fraction of wrongly detected outliers, for a number of outlier detection algorithms and establish an asymptotic normal and a Poisson theory for the gauge.
| Originalsprog | Engelsk |
|---|---|
| Tidsskrift | Scandinavian Journal of Statistics |
| Vol/bind | 43 |
| Nummer | 2 |
| Sider (fra-til) | 321-348 |
| Antal sider | 28 |
| ISSN | 0303-6898 |
| DOI | |
| Status | Udgivet - 1 jun. 2016 |
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